Confidential machine learning on untrusted platforms: a survey
Abstract With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning. Thus, sensitive dat...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2021-09-01
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| Series: | Cybersecurity |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42400-021-00092-8 |